Employment of gis and remote sensing to detect land use change Class Project FR 3262/5262 Dec 2011 Peder Engstrom Chad Sigler

Why China for a project? Project collaboration with Cargill Corporation and IonE/GLI on agricultural and land use practices China is a net food exporter – imports and exports are growing but still need to feed 1.3 billion people China’s capacity to continue food export growth is constrained by intense competition for limited resources by nonagricultural industry and other sectors of the economy Intensive use of chemicals – affects China’s future production and will lead to deteriorating environmental quality

Change detection: Urban vs. Rural Expansion Insight into agricultural use Data to provide assistance on a productive and sustainable approach to farming Possibility of follow- on study

Province Area: 156,700 square kilometers Study Area is 7,480 square kilometers Population: million (2000 population census) Shandong major agricultural province: grains, meat, fruits, aquatic products Mix of industry and rural Terrain: wetlands, plains, mountains, and coastal areas

Shandong Province, China

SourceSatelliteSensorImagery USGS Global Visualization Viewer Landsat 7 (L7) ETM+ multi- spectral 2004, 7 Bands, 185 Km x 185 km, 30 meter Res USGS Global Visualization Viewer Landsat 7 (L7) ETM+ multi- spectral 2009, 7 Bands, 185 Km x 185 Km, 30 meter Res GeoEyeGeoEye01Panchrom atic + multi- spectral 2009, 4 Bands, 3 Km x 3 Km, 1.5 Meter Res (resample images to.5 meter resolution)

Mosaic Landsat Data Set Shandong Province, China Lat: 34.6 Long: Study areaAOI

Mosaic Landsat Data Set Shandong Province, China Lat: 34.6 Long: 115.0

ESRI ArcGIS 10.0 Making of maps Image Processing ERDAS IMAGINE 2010 Image Processing Mosaic Tool Subset Image Analysis Tools (Unsupervised, Supervised, Accuracy Assessment)

Performed unsupervised classification on 2004 and 2009 Landsat imagery why? Large AOI, less human bias, time restriction Followed ISODATA method 7 classes, 10 passes Landsat 2009Thematic Map 2009

Performed change detection and analysis (Thematic Change) significant increase in conversion of rural land to urban in five years – class adjustments/combinations

Landsat 2004 Landsat 2009

Reference data derived from 1.5 meter multi, GE01, 3Km x 3Km, Randomly selected sample points Class values hidden from actors (low bias) Relatively small sample of total study area

Level of difficulty using Landsat imagery Had to scale back project and adjust sampling dates based on imagery availability No “ground truth” ability – limited ability to assess objects Google great resource but limited data sets No “free” commercial imagery (although we did crack one nut…) Used Imagine for most of the project – would consider using ArcGIS in future for like projects Possibilities for further future work on this study

Campbell, J., Wynne, R. (2011) Introduction to Remote Sensing (Fifth Edition). New York: Guilford Press Ge, J., Huang, Z., Liang, Y., Shen, Y., Tang, C., Tateishi, R., Xiao, J. Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape and Urban Planning, 75, (2006) Harvard University Arts and Science, China GIS data base (CHGS Version 4). Retrieved from Knight, J. (2011) ERDAS Imagine Lab Guides. Retrieved from University of Minnesota FR & 002 Remote Sensing of Natural Resources and Environment (Fall 2011) class website: US Government. CIA World Factbook. Retrieved from: world-factbook/geos/ch.htmlhttps:// world-factbook/geos/ch.html Walter, V. Object-based classification of remote sensing data for change detection. Journal Photogrammetry & Remote Sensing, 58, (2004)